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bayesGARCH: Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations in R

David Ardia; Lennart F. Hoogerheide


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.231327", 
  "container_title": "The R Journal", 
  "title": "bayesGARCH: Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations in R", 
  "issued": {
    "date-parts": [
      [
        2017, 
        1, 
        5
      ]
    ]
  }, 
  "abstract": "<p>The package bayesGARCH implements in R (R Core Team, 2016) the Bayesian estimation procedure described in Ardia (2008, chapter 5) for the GARCH(1,1) model with Student-t innovations. The approach consists of a Metropolis-Hastings (MH) algorithm where the proposal distributions are constructed from auxiliary ARMA processes on the squared observations. This methodology avoids the time-consuming and difficult task, especially for non-experts, of choosing and tuning a sampling algorithm. We refer the user to Ardia (2008) and Ardia and Hoogerheide (2010) for illustrations. The latest version of the package is available at https://github.com/ArdiaD/bayesGARCH.</p>", 
  "author": [
    {
      "family": "David Ardia"
    }, 
    {
      "family": "Lennart F. Hoogerheide"
    }
  ], 
  "page": "41-47", 
  "volume": "2", 
  "version": "v2.0.4", 
  "type": "article", 
  "issue": "2", 
  "id": "231327"
}
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